Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Xueling ShangMinglong ZhangDehui SunYufang LiangTony BadrickYanwei HuQingtao WangRui Zhou

Published in: Clinical chemistry and laboratory medicine (2024)

The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Keyphrases

quality control
artificial intelligence
machine learning
big data
deep learning
case report
loop mediated isothermal amplification
real time pcr
label free
electronic health record
convolutional neural network